Effects of Changing ActiGraph Bandpass Filter Width For Detecting Walking and Running

LaMunion, Samuel R.; Dr, Bassett; Se, Crouter

doi:10.1249/01.mss.0000486644.57610.81

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“…As average counts or acceleration increased, the absolute difference (100 minus 30 (Figure 2(a)). Conversely, differences remained uniform as counts/15-sec increased for the wrist-worn monitor (Figure 2 ActiGraph accelerometers use a proprietary frequency band-pass filter to attenuate signals considered to be outside of the range of normal human movement, although this has also been shown to attenuate signals recorded during vigorous-intensity activity (26,27).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Effect of sampling rate on acceleration and counts of hip- and wrist-worn ActiGraph accelerometers in children

et al. 2019

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Sampling rate (Hz) of ActiGraph accelerometers may affect processing of acceleration to activity counts when using a hip-worn monitor, but research is needed to quantify if sampling rate affects actual acceleration (mg's), when using wrist-worn accelerometers and during non-locomotive activities. Objective: To assess the effect of ActiGraph sampling rate on total counts/15-sec and mean acceleration and to compare differences due to sampling rate between accelerometer wear locations and across different types of activities. Approach: Children (n=29) wore a hip-and wrist-worn accelerometer (sampled at 100 Hz, downsampled in MATLAB to 30 Hz) during rest/transition periods, active video games, and a treadmill test to volitional exhaustion. Mean acceleration and counts/15-sec were computed for each axis and as vector magnitude. Main Results: There were mostly no significant differences in mean acceleration. However, 100 Hz data resulted in significantly more total counts/15-sec (mean bias 4-43 counts/15-sec across axes) for both the hip-and wrist-worn monitor when compared to 30 Hz data. Absolute differences increased with activity intensity (hip: r=0.46-0.63; wrist: r=0.26-0.55) and were greater for hipversus wrist-worn monitors. Percent agreement between 100 and 30 Hz data was high (97.4-99.7%) when cut-points or machine learning algorithms were used to classify activity intensity. Significance: Our findings support that sampling rate affects the generation of counts but adds that differences increase with intensity and when using hip-worn monitors. We recommend researchers be consistent and vigilantly report the sampling rate used, but note that classifying data into activity intensities resulted in agreement despite differences in sampling rate.

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Section: Accepted Manuscriptmentioning

confidence: 99%

“…as they are less affected by the narrow band-pass filter used by ActiGraph(30). Interestingly, as shown when comparing Figures 3(a) and 3(b), the wrist-worn monitor had larger absolute differences in counts/15-sec during active video games compared to the hip-worn monitor, which could be due to increased reliance on arm movements (31).…”

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confidence: 95%